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.0em plus.0ex minus.2ex-.5emLVIS Benchmark. We evaluate our approach on the LVIS dataset [1]} which contains a large and diverse set of 1203 object categories suitable for open-vocabulary detection. Following the existing works [2]}, [3]}, we treat the frequent and common categories as the base categories \(C_B\)  for training, and hold out the rare categories as novel categories \(C_N\)  for testing. Mask AP\(_r\)  is the main metric we benchmark on. To ensure reproducibility, we report the mean of 5 independent runs following the protocol of [2]} and the best practice of LVIS challenge [1]}. For fair comparison, we adopt the same Mask R-CNN head architecture as [2]} and use the same large scale jittering recipe [7]}.
